Artificial Intelligence in Renewable Energy: A Review of Predictive Maintenance and Energy Optimization

Abstract

The integration of Artificial Intelligence (AI) into renewable energy systems represents a transformative step in enhancing the efficiency, reliability, and sustainability of clean energy technologies. This review explores the roles and applications of AI techniques—including Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), and ensemble models like XGBoost—in predictive maintenance and energy optimization. Through a comprehensive analysis of recent studies, the review highlights how AI improves system performance by enabling early fault detection, optimizing energy distribution, and managing storage efficiently. Predictive maintenance driven by AI can reduce unplanned downtime by up to 35% and enhance energy output by approximately 8.5%. In energy optimization, AI models forecast demand and control load distribution, significantly contributing to smart grid development. However, several challenges remain, particularly in Indonesia, including limited high-quality data, high computational demands, system interoperability issues, and a lack of regulatory and human resource readiness, reducing unplanned downtime by up to 35% and increasing energy output by approximately 8.5%, as reported in previous studies. The review concludes that successful implementation requires strategic investment in digital infrastructure, inter-sectoral collaboration, and pilot projects to ensure sustainable AI adoption in Indonesia's renewable energy sector.